Deep and low-level feature based attribute learning for person re-identification

Chen, Yiqiang; Duffner, Stefan; Stoian, Andrei; Dufour, Jean-Yves; Başkurt, Atilla

doi:10.1016/j.imavis.2018.09.001

Cited by 33 publications

(5 citation statements)

References 4 publications

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“…Furthermore, DL has been broadly applied in material science to improve the targeted properties 21 , 26 – 33 . One form of DL models that has been extensively used for feature extraction in various applications such as image, video, voice, and natural language processing is Convolutional Neural Networks (CNN) 34 – 37 . In materials science, CNN has been used for various image-related problems.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach for chemistry and processing history prediction from materials microstructure

Farizhandi

Betancourt

Mamivand

2022

Sci Rep

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Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe–Cr–Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe–Cr–Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth.

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Section: Introductionmentioning

confidence: 99%

Deep learning approach for chemistry and processing history prediction from materials microstructure

Farizhandi

Betancourt

Mamivand

2022

Sci Rep

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“…Attribute based models: An attribute-based method is proposed by Chen et al [80] that uses embedding learning to drive attributes and identity annotations from a person's appearance, whereby two embedding-based CNNs are learned, simultaneously. The pre-defined attributes of this work, mainly, rely on pedestrian's appearance in order to extract similar cues between the same person -i.e., if a pedestrian wears a red T-shirt and/or a black backpack at the same time.…”

Section: Triplet-loss Methodsmentioning

confidence: 99%

Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?

Lavi,

Ullah,

Fatan

et al. 2020

Preprint

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Intelligent video-surveillance (IVS) is currently an active research field in computer vision and machine learning and provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one of the most critical problems in IVS, and it consists of recognizing whether or not an individual has already been observed over a camera in a network. Solutions to PReID have myriad applications including retrieval of video-sequences showing an individual of interest or even pedestrian tracking over multiple camera views. Different techniques have been proposed to increase the performance of PReID in the literature, and more recently researchers utilized deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time. Given the importance and wide range of applications of re-identification solutions, our objective herein is to discuss the work carried out in the area and come up with a survey of state-of-the-art DNN models being used for this task. We present descriptions of each model along with their evaluation on a set of benchmark datasets. Finally, we show a detailed comparison among these models, which are followed by some discussions on their limitations that can work as guidelines for future research.

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“…Hence each sub-net stream learns to resolve different scenarios of the problem. In [80] attribute based approach was used to approach the challenge of large visual variations and spatial shifts. Large spatial shifts are caused by different pose variations and camera views.…”

Section: Cnn-based Approachesmentioning

confidence: 99%

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

Zahra¹,

Perwaiz²,

Shahzad³

et al. 2022

Preprint

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Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views. Owing to its potential in various applications and research significance, a plethora of deep learning based re-Id approaches have been proposed in the recent years. However, there exist several vision related challenges, e.g., occlusion, pose scale & viewpoint variance, background clutter, person misalignment and cross-domain generalization across camera modalities, which makes the problem of re-Id still far from being solved. Majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. In this context, a comprehensive review of current re-ID approaches in solving theses challenges is needed to analyze and focus on particular aspects for further advancements. At present, such a focused review does not exist and henceforth in this paper, we have presented a systematic challenge-specific literature survey of 230+ papers between the years of 2015-21. For the first time a survey of this type have been presented where the person re-Id approaches are reviewed in such solution-oriented perspective. Moreover, we have presented several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing person re-Id research and eventually help to develop practical real world solutions. Scope/Objective of the ReviewIn this paper, we have targeted the most popular challenges in person re-id to perform systematic challenge-wise review of the published approaches. In this context, we have collected papers from top conferences and journals for the years from 2015 to 2021. The progress in papers addressing each challenge and its influence on published results is

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Deep and low-level feature based attribute learning for person re-identification

Cited by 33 publications

References 4 publications

Deep learning approach for chemistry and processing history prediction from materials microstructure

Deep learning approach for chemistry and processing history prediction from materials microstructure

Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?

Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends

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